WO2023281699A1 - データ分析処理装置、データ分析処理方法、およびデータ分析処理プログラム - Google Patents

データ分析処理装置、データ分析処理方法、およびデータ分析処理プログラム Download PDF

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WO2023281699A1
WO2023281699A1 PCT/JP2021/025786 JP2021025786W WO2023281699A1 WO 2023281699 A1 WO2023281699 A1 WO 2023281699A1 JP 2021025786 W JP2021025786 W JP 2021025786W WO 2023281699 A1 WO2023281699 A1 WO 2023281699A1
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data
multidimensional
event
multidimensional cube
cube
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English (en)
French (fr)
Japanese (ja)
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哲 八木
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NTT Inc
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Nippon Telegraph and Telephone Corp
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Priority to PCT/JP2021/025786 priority Critical patent/WO2023281699A1/ja
Priority to JP2023532982A priority patent/JP7700859B2/ja
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models

Definitions

  • the present invention relates to a data analysis processing device, a data analysis processing method, and a data analysis processing program.
  • Data analysis processing that maps data embodying real-world phenomena that change temporally and/or spatially due to generation and disappearance and/or state transitions into a multi-dimensional cube and analyzes them by online analysis processing operations. device is known.
  • the data analysis processing device uses, for example, the method disclosed in Non-Patent Document 1. The method performs the following processes 1 and 2.
  • the data representing the properties of the event and the dimensional data identifying the data representing the properties form a multidimensional cube.
  • the data that make up the multidimensional cube are analyzed by performing on-line analytical processing operations on the multidimensional cube.
  • Conventional data analysis processing devices have limited points of focus for analyzing the data that constitutes the multidimensional cube. For example, when a conventional data analysis processing device constructs a multidimensional cube, data representing the characteristics of an event and dimensional data identifying the data representing the characteristics are used. Do not use identifiers or data representing the structure of events.
  • the present invention has been made with a focus on the above circumstances, and its purpose is to analyze the data by focusing on the structure of the event that is the information source of the data when analyzing the data that constitutes the multidimensional cube.
  • An object of the present invention is to provide an analysis processing device, a data analysis processing method, and a data analysis processing program.
  • One aspect of the present invention is an online analysis of real-world phenomena that change in at least one of time and space due to at least one of creation and disappearance and state transitions, by mapping data that embodies the event into a multidimensional cube. It is a data analysis processor that analyzes by processing operations.
  • the data analysis processing device uses time-dimensional data and space-dimensional data embodying temporal and spatial changes due to at least one of the generation and disappearance and the state transition as data embodying the event, and subject-dependent data.
  • a multidimensional database management unit that stores and manages the multidimensional database, and an existing An online analysis processing operation execution unit that references/aggregates data that constitutes the multidimensional cube or generates a new multidimensional cube, and data representing the structure of the event that constitutes the multidimensional cube.
  • the multidimensional database management unit includes a structural manipulation execution unit for increasing or decreasing data constituting the multidimensional cube.
  • One aspect of the present invention is an online analysis of real-world phenomena that change in at least one of time and space due to at least one of creation and disappearance and state transitions, by mapping data that embodies the event into a multidimensional cube. It is a data analysis processing method that analyzes by processing operations.
  • the data analysis processing method includes time-dimensional data and space-dimensional data embodying temporal and spatial changes due to at least one of the generation and disappearance and the state transition as the data embodying the event, and depending on the subject.
  • the data representing multiple types of characteristics of the event dependent on the subject identified by the temporal dimension data, the spatial dimension data and the intrinsic dimension data Associated with the identifier of the event for identifying the event that is the information source of the data, and stored and managed in the multidimensional cube constructed for each subject together with the data representing the structure of the event. and referencing/aggregating the data constituting the existing multidimensional cube, or generating a new multidimensional cube, using the argument instructed by the client or other data constituting the multidimensional cube. and increasing or decreasing the data constituting the multidimensional cube by using the data representing the structure of the event constituting the multidimensional cube.
  • a data analysis processing program causes a computer to execute the function of each component of the data analysis processing device described above.
  • a data analysis processing device a data analysis processing method, and a data analysis processing that can analyze data by focusing on the structure of an event that is an information source of the data when analyzing data that constitutes a multidimensional cube.
  • a program is provided.
  • FIG. 1 is a block diagram showing an example of the configuration of a data analysis processing device according to an embodiment.
  • FIG. 2 is a diagram showing an example of data representing a time dimension, a space dimension, an eigendimension, and characteristics that constitute a multidimensional cube.
  • FIG. 3 is a diagram showing an example of tabular data representing the network structure of events and a schematic diagram of the structure of events.
  • FIG. 4 is a diagram showing an example of tabular data representing the network structure and hierarchical structure of events and a schematic diagram of the structure of events.
  • FIG. 5 is a diagram showing an example of tabular data representing the network structure and hierarchical structure of events and a schematic diagram of the structure of events.
  • FIG. 1 is a block diagram showing an example of the configuration of a data analysis processing device according to an embodiment.
  • FIG. 2 is a diagram showing an example of data representing a time dimension, a space dimension, an eigendimension, and characteristics that constitute a multidimensional cube.
  • FIG. 3 is
  • FIG. 6 is a diagram showing an example of tabular data representing the network structure and hierarchical (abstraction) structure of events and a schematic diagram of the structure of events.
  • FIG. 7 is a diagram showing an example of a schematic diagram representing the network structure and hierarchical/hierarchical (abstraction) structure of events.
  • FIG. 8 is a sequence diagram for explaining an example of the operation of the data analysis processing device.
  • FIG. 9 is a flowchart for explaining an example of details of the operation of the multidimensional database management unit.
  • FIG. 10 shows non-normalized tabular data as an example of data representing the time dimension, spatial dimension, eigendimension, and characteristics that constitute the multidimensional cubes of the generation source and the generation destination, and data representing the structure of the event.
  • FIG. 10 shows non-normalized tabular data as an example of data representing the time dimension, spatial dimension, eigendimension, and characteristics that constitute the multidimensional cubes of the generation source and the generation destination, and data representing the structure of the event.
  • FIG. 3 shows an example of tabular data and a schematic diagram of the structure of an event.
  • FIG. 11 is a schematic diagram illustrating an example of details of the operation when increasing the data constituting the multidimensional cube.
  • FIG. 12 is a diagram showing an example of tabular data representing the structure of an event and a schematic diagram of the structure of the event in the case of FIG.
  • FIG. 13 is a schematic diagram for explaining an example of details of the operation when reducing the data constituting the multidimensional cube.
  • FIG. 14 is a diagram showing an example of tabular data representing the structure of events and a schematic diagram of the structure of events in the case of FIG.
  • FIG. 15 is a flowchart for explaining an example of detailed operations of the multidimensional database management unit.
  • Figure 16 shows the increase in the data that make up the multidimensional cube when the data representing the time dimension, spatial dimension, eigendimension, and characteristics that make up the multidimensional cube are stored as denormalized tabular data.
  • FIG. 10 is a schematic diagram for explaining an example of details of the operation in the case of allowing.
  • FIG. 17 shows how to increase the data that make up a multidimensional cube when the data representing the time dimension, spatial dimension, eigendimension, and characteristics that make up the multidimensional cube are stored as normalized tabular data.
  • FIG. 10 is a schematic diagram for explaining an example of details of the operation in the case; FIG.
  • FIG. 18 shows the reduction of data that constitutes a multidimensional cube when the data representing the time dimension, spatial dimension, eigendimension, and characteristics that constitute the multidimensional cube are stored as non-normalized tabular data.
  • FIG. 10 is a schematic diagram for explaining an example of details of the operation in the case of allowing.
  • FIG. 19 shows how to reduce the data constituting a multidimensional cube when the data representing the time dimension, spatial dimension, eigendimension, and characteristics constituting the multidimensional cube are stored as normalized tabular data.
  • FIG. 10 is a schematic diagram for explaining an example of details of the operation in the case;
  • FIG. 20 is a block diagram illustrating an example of the hardware configuration of the data analysis processing device according to the embodiment;
  • FIG. 1 is a block diagram showing an example of the configuration of a data analysis processing device 10 according to an embodiment.
  • the data analysis processor 10 has an online analytical processing operation execution unit 12 , a structural operation execution unit 14 , a multidimensional database management unit 16 and a multidimensional database 18 .
  • the data analysis processing device 10 maps data embodying real-world phenomena that temporally and/or spatially change due to generation and disappearance and/or state transitions to a multidimensional cube, and performs online analysis processing operations. It is a device for analysis.
  • online analytical processing is hereinafter referred to as OLAP (Online Analytical Processing).
  • the online analysis processing operation is called an OLAP operation
  • the online analysis processing operation execution section 12 is called an OLAP operation execution section 12 .
  • the multidimensional database management unit 16 uses time-dimensional data and space-dimensional data that embody temporal and spatial changes due to at least one of generation, disappearance, and state transition as data that embody events, and subject-dependent data. Multiple types of eigendimensional data and data representing multiple types of characteristics of the subject-dependent events identified by the temporal, spatial, and eigendimensional data are the sources of the data.
  • the multidimensional database 18 stores and manages the multidimensional database 18 as a multidimensional cube constructed for each subject together with the identifier of the event for identifying the event and the data representing the structure of the event.
  • Fig. 2 is a diagram showing an example of data representing the characteristics of the time dimension, space dimension, eigendimension, and events that make up the multidimensional cube.
  • the upper part (a) of FIG. 2 shows non-normalized tabular data, and the lower part (b) of FIG. 2 shows normalized tabular data.
  • Each tabular data has a data identifier, an event identifier data, and a data value associated therewith for each serial number.
  • Figures 3 to 6 are diagrams showing examples of data representing the structure of an event that constitutes a multidimensional cube and an example of a schematic diagram of the structure of the event. Also, FIG. 7 is a diagram showing an example of a schematic diagram of the structure of an event.
  • the data representing the structure of the event is shown as tabular data.
  • the tabular data consists of a bidirectionally linked list of two event identifiers (self and counter) representing the structure between the two events, and information on the type and attribute of the structure between the two events. .
  • the type information indicates the type of structure
  • the attribute information indicates the use of the structure.
  • Attribute values may include time information when the structure of the event is established. In this case, the attribute value can be held as a unique value of the attribute or as a time-dimensional data value. Attribute values may have a hierarchical structure.
  • FIG. 7 the schematic diagrams of the structure of events show the structure between two events identified by identifiers.
  • the structure between two events identified by an identifier is based on a bidirectionally linked list of the two event's identifiers (self and opposite).
  • 3 to 7 also show event identifiers. This identifier is associated with data representing the structure of the event.
  • FIG. 7 also shows event case names, structure types, and attributes.
  • FIG. 3 is a diagram showing an example of tabular data representing the network structure of events and a schematic diagram of the structure of events.
  • the structure in FIG. 3 may be a transportation network, a power/gas/water network, a communication network, or the like.
  • event identifiers p1 and p2 are delivery points/intersections
  • event identifier p3 is road/railway/river/sea/airway.
  • event identifiers p1 and p2 are electricity/gas/water distribution facilities and event identifier p3 is a utility utility utility pipe.
  • event identifiers p1 and p2 are communication facilities
  • event identifier p3 is a tunnel/telegraph pole.
  • the network structure data (row) p1p3 shows the structure of p3 as seen from p1, the type indicates dependency, and the attribute indicates a delivery network.
  • Network structure data (row) p3p1 shows the structure of p1 viewed from p3, the type indicates dependency, and the attribute indicates a distribution network.
  • "dependence” is simply referred to as "dependent relationship”.
  • the hierarchical structure of attribute values of the distribution network has normal, spare, and temporary under distribution.
  • FIG. 4 is a diagram showing an example of tabular data representing the network structure and hierarchical structure of events and a schematic diagram of the structure of events.
  • the structure in FIG. 4 is a temporary transportation network or the like.
  • the event identifiers p1 and p2 are delivery points/intersections
  • the event identifier p3 is road/railway/river/sea route/airway
  • the event identifiers p4 and p5 are warehouse/signal.
  • the event identifier p6 is a road sign/river sign/light wave sign/radio wave sign.
  • the network structure data (row) p1p3 shows the structure of p3 as seen from p1, the type indicates a dependent relationship, and the attribute indicates a temporary delivery network.
  • Network structure data (row) p3p1 shows the structure of p1 viewed from p3, the type indicates a dependent relationship, and the attribute indicates a temporary delivery network.
  • the hierarchical structure of attribute values in the network structure has normal, spare, and temporary under delivery.
  • Hierarchical structure data (row) p3p6 shows the structure of p6 as seen from p3, the type indicates has-a (aggregating relationship), and the attribute indicates temporary components. is shown.
  • Hierarchical structure data (row) p6p3 shows the structure of p3 as seen from p6, the type indicates part-of (constituting relationship), and the attribute indicates temporary component. is shown.
  • “has-a” is simply referred to as "aggregating relationship”
  • “part-of” is simply referred to as "composing relationship”.
  • the hierarchical structure of attribute values in the hierarchical structure has constant and temporary at the bottom of the configuration.
  • FIG. 5 is a diagram showing an example of tabular data representing the network structure and hierarchical structure of events and a schematic diagram of the structure of events.
  • the structure of FIG. 5 may be a permanent power grid/gas grid/water grid, telecommunication grid, or the like.
  • event identifiers p1 and p2 are electricity/gas/water distribution facilities
  • event identifier p3 is a utility utility drain
  • event Identifiers p7 and p8 of are electric/gas/water equipment
  • event identifier p9 is electric wire/gas pipe/water pipe.
  • the event identifiers p1 and p2 are communication facilities
  • the event identifier p3 is a tunnel/telegraph pole
  • the event identifiers p7 and p8 are communication equipment
  • the event identifier p9 is the communication cable.
  • the network structure data (row) p1p3 shows the structure of p3 as seen from p1, the type indicates a dependent relationship, and the attribute indicates a regular delivery network.
  • Network structure data (row) p3p1 shows the structure of p1 viewed from p3, the type indicates a dependent relationship, and the attribute indicates a regular delivery network.
  • the hierarchical structure of attribute values in the network structure has normal, spare, and temporary under delivery.
  • Hierarchical data (row) p3p9 shows the structure of p9 as seen from p3, where the type indicates an aggregating relationship and the attribute indicates a permanent component.
  • Hierarchical structure data (row) p9p3 shows the structure of p3 as viewed from p9, where the type indicates a forming relationship and the attribute indicates a permanent component.
  • the hierarchical structure of attribute values in the hierarchical structure has constant and temporary at the bottom of the configuration.
  • FIG. 6 is a diagram showing an example of tabular data representing the network structure and hierarchical (abstraction) structure of events, and a schematic diagram of the structure of events.
  • the structure in FIG. 6 may be a permanent power grid/gas/water grid, telecommunication grid, or the like.
  • the event identifiers p7 and p8 are electric/gas/water appliances
  • the event identifier p9 is a power line/gas/water pipe.
  • the event identifier p10 is the requirement specification.
  • the structure is a permanent communication network, for example, event identifiers p7 and p8 are communication equipment, event identifier p9 is a communication cable, and event identifier p10 is a requirement specification.
  • the network structure data (row) p7p9 shows the structure of p9 as seen from p7, the type indicates a dependent relationship, and the attribute indicates a regular delivery network.
  • Network structure data (row) p9p7 shows the structure of p7 viewed from p9, the type indicates a dependent relationship, and the attribute indicates a regular delivery network.
  • the hierarchical structure of attribute values in the network structure has normal, spare, and temporary under delivery.
  • the data (row) p7p10 of the hierarchical (abstraction) structure shows the structure of p10 as seen from p7, the type indicates Is-a (inheritance relationship), and the attribute is the specification that p7 follows. It shows that The data (row) p10p7 of the hierarchical (abstraction) structure shows the structure of p7 viewed from p10, the type indicates Is-a (inherited relationship), and the attribute is the product following p10. indicates that there is The hierarchical structure of attribute values in the hierarchical (abstraction) structure has design below specification and product below design.
  • FIG. 7 is a diagram showing an example of a schematic diagram representing the network structure and hierarchical/hierarchical (abstraction) structure of events.
  • the structure of FIG. 7 is a communication network with communication protocol stacks.
  • event identifiers p7 and p8 are L2 switches
  • event identifiers p9, p13 and 14 are communication cables
  • event identifier p10 is a requirement specification
  • event identifiers p11, p12, P19 is the router
  • event identifiers p15 and p16 are the L2 control boards
  • event identifier p17 is the L2 routing program
  • event identifier p18 is the L3 routing program.
  • L2 and L3 mean the second layer and the third layer, respectively.
  • the OLAP operation execution unit 12 receives the OLAP operation and arguments sent from the client 40, and instructs the multidimensional database management unit 16 to operate the multidimensional data accordingly. Also, it receives the operation result of the multidimensional data from the multidimensional database management unit 16 and transmits the operation result to the client 40 .
  • the structure manipulation execution unit 14 receives the structure manipulation and arguments transmitted from the client 40, and instructs the multidimensional database management unit 16 to manipulate the multidimensional data accordingly. Further, the structural manipulation execution unit 14 receives the manipulation result of the multidimensional data from the multidimensional database management unit 16 and transmits the manipulation result to the client 40 .
  • the multidimensional database management unit 16 refers to/aggregates data constituting an existing multidimensional cube or generates a new multidimensional cube in accordance with an operation instruction from the OLAP operation execution unit 12, and applies the operation result to an OLAP operation. Return to execution unit 12 .
  • the multidimensional database management unit 16 increases or decreases the data constituting the multidimensional cube by using the data representing the structure of the events constituting the multidimensional cube in accordance with the operation instruction from the structural manipulation execution unit 14. , and returns the operation result to the structure operation execution unit 14 .
  • FIG. 8 is a sequence diagram for explaining an example of the operation of the data analysis processing device 10. As shown in FIG.
  • OLAP operation execution unit 12 receives an OLAP operation and an argument from the client 40, the multidimensional database management unit 16 responds to them. Directs manipulation of dimensional data.
  • the multidimensional database management unit 16 refers to/aggregates data constituting a multidimensional cube or creates a new multidimensional cube in accordance with an instruction to manipulate multidimensional data.
  • the multidimensional database management unit 16 returns the operation result to the OLAP operation execution unit 12.
  • the OLAP operation execution unit 12 repeats the above instructions to the multidimensional database management unit 16 according to the content of the received OLAP operation and argument, as indicated by "LOOP" surrounded by a dashed line in FIG.
  • the OLAP operation execution unit 12 returns the operation result of the OLAP operation to the client 40 when the final result corresponding to the OLAP operation and the argument has been obtained.
  • the structural operation execution unit 14 receives structural operations and arguments from the client 40, and sends the multidimensional database management unit 16 accordingly. Directs manipulation of dimensional data.
  • the multidimensional database management unit 16 increases or decreases the data forming the multidimensional cube by using the data representing the structure of the events forming the multidimensional cube in accordance with the instruction for manipulating the multidimensional data.
  • the multidimensional database management unit 16 returns the operation result to the structure operation execution unit 14 .
  • Structural operation execution unit 14 repeats the above instructions to multidimensional database management unit 16 according to the content of the received structural operation and argument, as indicated by "LOOP" surrounded by a dashed line in FIG.
  • the structure manipulation execution unit 14 returns the manipulation result of the structure manipulation to the client 40 when the final manipulation result corresponding to the content of the structure manipulation and the argument has been obtained.
  • the multidimensional database management unit 16 responds to the multidimensional data manipulation instruction from the structure manipulation execution unit 14 by performing the OLAP manipulation on the source multidimensional cube and the destination multidimensional cube.
  • the data of the multidimensional cube of the generation source is used as the original, and the data that constitutes the multidimensional cube of the generation destination is increased based on the data types and attribute values that represent the structure of the phenomena that make up the multidimensional cube of the generation destination. Or decrease.
  • FIG. 9 is a flowchart for explaining the details of the operation of the multidimensional database management unit 16 according to this operation example.
  • step S11 the multidimensional database management unit 16 waits for reception of a multidimensional data manipulation instruction for structure manipulation from the structure manipulation execution unit 14. That is, the multidimensional database management unit 16 maintains the reception waiting state until it receives a multidimensional data manipulation instruction for structural manipulation.
  • step S12 when the multidimensional data manipulation instruction for structural manipulation is received, the multidimensional database management unit 16 determines the type of the manipulation instruction.
  • step S12 if the operation instruction is an instruction to increase the data constituting the multidimensional cube of the generation destination, the multidimensional database management unit 16 executes the processing of steps S13a to S13c below.
  • step S13a data that satisfies the conditions for the type and attribute values is extracted from the data representing the structure of the events that make up the multidimensional cube to be generated.
  • step S13b "the identifier of the event that is the information source of the data to be added" is extracted. At this time, any event identifier may be selected.
  • step S13c data whose information source is the event of the extracted identifier is extracted from the source multidimensional cube and added to the destination multidimensional cube.
  • step S12 determines whether the operation instruction is an instruction to reduce the data that constitutes the multidimensional cube of the generation destination. If the result of determination in step S12 is that the operation instruction is an instruction to reduce the data that constitutes the multidimensional cube of the generation destination, the multidimensional database management unit 16 performs the following steps S14a to S14c. Run.
  • step S14a data that satisfies the conditions for the type and attribute values is extracted from the data representing the structure of the events that make up the multidimensional cube to be generated.
  • step S14b "the identifier of the event that is the information source of the data to be deleted" is extracted. At this time, any event identifier may be selected.
  • step S14c data whose information source is the event of the extracted identifier is deleted from the destination multidimensional cube.
  • step S15 the multidimensional database management unit 16 returns the operation result to the structure operation execution unit 14 after the processing of steps S13a to S13c or the processing of steps S14a to S14c is completed.
  • FIG. 10 shows non-normalized tabular data as an example of data representing the time dimension, spatial dimension, eigendimension, and characteristics that constitute the multidimensional cubes of the generation source and the generation destination, and data representing the structure of the event.
  • FIG. 3 shows an example of tabular data and a schematic diagram of the structure of an event.
  • a multi-dimensional cube whose subject is damaged/failed facilities is generated from a multi-dimensional cube whose subject is all facilities by specifying data conditions of time dimension and space dimension by OLAP operation. is the case.
  • source data and a schematic diagram are shown on the left side
  • destination data and a schematic diagram are shown on the right side.
  • FIG. 11 is a schematic diagram for explaining an operation example in the case of increasing the data constituting the multidimensional cube of the generation destination, with the state of FIG. 10 as a precondition.
  • step S13a data (rows) whose type is dependency is extracted from the data representing the structure of the events forming the multidimensional cube of the generation destination.
  • step S13b the set operation (difference) extracts the identifier of the event that exists in the identifier of the event (opposite) and does not exist in the identifier of the event (self). In other words, it extracts the identifier of the event that is in a dependent relationship but is not the information source of the data that constitutes the multidimensional cube to be generated. This is the "identifier of the event that is the information source of the data to be added".
  • step S13c data whose information source is the event of the extracted identifier is extracted from the source multidimensional cube and added to the destination multidimensional cube.
  • FIG. 12 is a diagram showing an example of data representing the structure of an event and a schematic diagram of the structure of the event when processed as described above. This example is, for example, a case of adding (registering) equipment affected by damaged/failed equipment, in other words, indirectly damaged/failed equipment based on the investigation results.
  • data and a schematic diagram before addition are shown on the left side
  • data and a schematic diagram after addition are shown on the right side.
  • FIG. 13 is a schematic diagram for explaining an operation example when reducing the data that constitutes the multidimensional cube of the generation destination, with the state in the case of FIG. 11 as a precondition.
  • step S14a data (rows) whose type is dependency is extracted from the data representing the structure of the events forming the multidimensional cube to be generated.
  • step S14b-1 of step S14b the set operation (difference) is used to extract event identifiers that exist in the event identifier (opposite) and do not exist in the event identifier (self).
  • sub-step S14b-2 of step S14b the event identifier of the event identifier (self) paired with the extracted event identifier is extracted.
  • the identifier (self) of the event is extracted, with the identifier of the event that is not the information source of the data constituting the multidimensional cube of the generation destination being the identifier (opposite) of the event.
  • This is the "identifier of the event that is the source of the data to be deleted”.
  • step S14c data whose information source is the event of the extracted identifier is deleted from the destination multidimensional cube.
  • FIG. 14 is a diagram showing an example of data representing the structure of an event and a schematic diagram of the structure of the event when processed as described above. This example is, for example, the case of deleting (withdrawing the registration of) equipment affected by the damaged/failed equipment, in other words, indirectly damaged/failed equipment, taking the opportunity of restoration work.
  • data and a schematic diagram before deletion are shown on the left side
  • data and a schematic diagram after deletion are shown on the right side.
  • the multidimensional database management unit 16 responds to the multidimensional data manipulation instruction from the structure manipulation execution unit 14 by performing the OLAP manipulation on the source multidimensional cube and the destination multidimensional cube.
  • the data of the multidimensional cube of the generation source is used as the original, and the data forming the multidimensional cube of the generation destination is increased or decreased.
  • the multidimensional database management unit 16 collects data types and attribute values that represent the structure of events that make up the multidimensional cube of the generation destination, time dimensions that make up the multidimensional cube of the generation source, spatial dimensions, specific Based on the values of the data representing the dimensions and characteristics, the data that make up the multidimensional cube of the generation destination is increased.
  • the multidimensional database management unit 16 collects data types and attribute values that represent the structure of events that make up the multidimensional cube of the generation destination, time dimensions that make up the multidimensional cube of the generation destination, spatial dimensions, specific Based on the values of data representing dimensions and characteristics, the data that constitutes the multidimensional cube of the destination is reduced.
  • FIG. 15 is a flowchart for explaining the details of the operation of the multidimensional database management unit 16 according to this operation example.
  • step S21 the multidimensional database management unit 16 waits for reception of a multidimensional data manipulation instruction for structure manipulation from the structure manipulation execution unit 14. That is, the multidimensional database management unit 16 maintains the reception waiting state until it receives a multidimensional data manipulation instruction for structural manipulation.
  • step S22 when the multidimensional data manipulation instruction for structural manipulation is received, the multidimensional database management unit 16 determines the type of the manipulation instruction.
  • step S22 if the operation instruction is an instruction to increase the data constituting the multidimensional cube of the generation destination, the multidimensional database management unit 16 executes the following steps S23a to S23d. .
  • step S23a data that satisfies the conditions for the type and attribute values is extracted from the data representing the structure of the events that make up the multidimensional cube to be generated.
  • step S23b "the identifier of the event that is the information source of the data to be added is extracted". At this time, any event identifier may be selected.
  • step S23c data whose information source is the event of the extracted identifier is extracted from the multidimensional cube of the generation source, and data to be added is selected based on the value of the data representing the time dimension, spatial dimension, unique dimension, and characteristics. do.
  • step S23d the selected data is added to the destination multidimensional cube.
  • step S22 determines whether the operation instruction is an instruction to reduce the data that constitutes the multidimensional cube of the generation destination. If the result of determination in step S22 is that the operation instruction is an instruction to reduce the data that constitutes the multidimensional cube of the generation destination, the multidimensional database management unit 16 performs the following steps S24a to S24d. Run.
  • step S24a data (rows) whose type and attribute values satisfy the conditions are extracted from the data representing the structure of the events that make up the multidimensional cube to be generated.
  • step S24b "the identifier of the event that is the information source of the data to be deleted" is extracted. At this time, any event identifier may be selected.
  • step S24c data whose information source is the event of the extracted identifier is extracted from the multidimensional cube of the generation destination, and data to be deleted is selected based on the value of the data representing the time dimension, spatial dimension, unique dimension, and characteristics. do.
  • step S24d the selected data is deleted from the destination multidimensional cube.
  • step S25 the multidimensional database management unit 16 returns the operation result to the structure operation execution unit 14 after the processing of steps S23a to S23d or the processing of steps S24a to S24d is completed.
  • FIG. 16 illustrates an operation example when the data representing the time dimension, spatial dimension, eigendimension, and characteristics that make up the multidimensional cube are stored as non-normalized tabular data in the same manner as in FIG. It is a schematic diagram.
  • the upper part (a) of FIG. 16 shows tabular data representing the time dimension, spatial dimension, eigendimension, and characteristics
  • the lower part (b) of FIG. 16 shows tabular data representing the structure of the event. .
  • the multidimensional database management unit 16 executes the processes of steps S23c and S23d as follows for the data representing the time dimension, space dimension, eigendimension, and characteristics shown in the upper part (a) of FIG. do.
  • step S23c data (rows) satisfying the conditions are selected from the source table.
  • the condition for the identifier of the event is that it is an element of the extracted "identifier of the event that is the source of the data to be added", and the conditions for the data representing the time dimension, the spatial dimension, the intrinsic dimension, and the characteristic are The data to be represented is within the specified range (f31 or f91).
  • the selected data (row) is added to the destination table.
  • the multidimensional database management unit 16 executes the processes of steps S23c and S23d as follows.
  • step S23c the "identifier of the event that is the information source of the data to be added" is replaced with the set of identifiers of the events of the data (rows) selected above, and the event of the identifier is selected as the information source from the generation source table. Filter the data (rows) that represent the structure to be In step S23d, the selected data (row) is added to the destination table. This results in a structure similar to that of FIG.
  • FIG. 17 is a schematic diagram for explaining an operation example when the data representing the time dimension, space dimension, eigendimension, and characteristics that constitute the multidimensional cube are stored as normalized tabular data, unlike FIG. It is a diagram.
  • the upper part (a) of FIG. 17 shows tabular data representing the time dimension, spatial dimension, eigendimension, and characteristics
  • the lower part (b) of FIG. 17 shows tabular data representing the structure of the event. .
  • the multidimensional database management unit 16 executes the processes of steps S23c and S23d as follows for the data representing the time dimension, space dimension, eigendimension, and characteristics shown in the upper part (a) of FIG. do.
  • step S23c has substeps S23c-1 to S23c-3.
  • sub-step S23c-1 the data (rows) that satisfy the conditions are selected by denormalizing the source table.
  • substep S23c-2 a table is created of the identifiers of the event and the identifiers of the data representing the time dimension, spatial dimension, eigendimension, and characteristics.
  • the condition for the identifier of the event is that it is an element of the extracted "identifier of the event that is the source of the data to be added", and the conditions for the data representing the time dimension, the spatial dimension, the intrinsic dimension, and the characteristic are The data to be represented is within the specified range (f31 or f91).
  • the data (rows) having the "temporal dimension, spatial dimension, unique dimension, identifier of data representing characteristics" in the table created above are converted to the normalized temporal dimension and spatial dimension of the generator. Filter from tables of data representing dimensions, intrinsic dimensions, and properties. Instead of creating a table consisting of event identifiers, temporal dimensions, spatial dimensions, unique dimensions, and identifiers of data representing characteristics as described above, the table of the generator is denormalized each time, and the temporal dimension, spatial dimension, It is also possible to select data (rows) that satisfy the conditions for each table of data representing unique dimensions and characteristics.
  • step S23d the selected data (row) is added to the data table representing the normalized time dimension, spatial dimension, eigendimension, and characteristics of the generation destination.
  • the operation example shown in the upper part (a) of FIG. 17 describes a time dimension table, but the same applies to a space dimension, an eigendimension, and a table of data representing characteristics.
  • the operation is the same as when it is stored as non-normalized tabular data.
  • the table is created by the processing of the above substeps S23c-1 and S23c-2, in substep S23c-3, the set of "event identifiers" in the created table indicates "the event that is the information source of the data to be added.” Identifier" is replaced, and the data (row) representing the structure whose information source is the event of the identifier is selected from the generation source table.
  • step S23d the selected data (row) is added to the destination table. This results in a structure similar to that of FIG.
  • FIG. 18 illustrates an example of operation when data representing the time dimension, spatial dimension, eigendimension, and characteristics that make up a multidimensional cube are stored as non-normalized tabular data in the same manner as in FIG. It is a schematic diagram.
  • the upper part (a) of FIG. 18 shows tabular data representing the time dimension, spatial dimension, eigendimension, and characteristics, and the lower part (b) of FIG. 18 shows tabular data representing the structure of the event. .
  • the multidimensional database management unit 16 executes the processes of steps S24c and S24d as follows for the data representing the time dimension, space dimension, eigendimension, and characteristics shown in the upper part (a) of FIG. do.
  • step S24c data (rows) that satisfy the conditions are selected from the destination table.
  • the condition for the identifier of the event is that it is an element of the extracted "identifier of the event that is the source of the data to be deleted", and the conditions for the data representing the time dimension, the spatial dimension, the intrinsic dimension, and the characteristic are The data to be represented is within the specified range (f31 or f91).
  • the selected data (row) is deleted from the destination table.
  • the multidimensional database management unit 16 executes the processes of steps S24c and S24d as follows.
  • step S24c the "identifier of the event that is the information source of the data to be deleted" is replaced with the set of event identifiers of the data (row) selected above, and the event of the identifier is selected as the information source from the generation destination table. Filter the data (rows) that represent the structure to be In step S24d, the selected data (row) is deleted from the destination table. This results in a structure similar to that of FIG.
  • FIG. 19 is a schematic for explaining an example of operation when data representing the time dimension, space dimension, eigendimension, and characteristics that make up a multidimensional cube are saved as normalized tabular data in the same manner as in FIG. It is a diagram.
  • the upper part (a) of FIG. 19 shows tabular data representing the time dimension, spatial dimension, eigendimension, and characteristics
  • the lower part (b) of FIG. 19 shows tabular data representing the structure of the event. .
  • the multidimensional database management unit 16 executes the processes of steps S24c and S24d as follows for the data representing the time dimension, space dimension, eigendimension, and characteristics shown in the upper part (a) of FIG. do.
  • step S24c has substeps S24c-1 to S24c-3.
  • sub-step S24c-1 the table to be generated is denormalized to select data (rows) that satisfy the conditions.
  • substep S24c-2 a table is created of the identifiers of the event and the identifiers of the data representing the time dimension, spatial dimension, eigendimension, and properties.
  • the condition for the identifier of the event is that it is an element of the extracted "identifier of the event that is the source of the data to be deleted", and the conditions for the data representing the time dimension, the spatial dimension, the intrinsic dimension, and the characteristic are The data to be represented is within the specified range (f31 or f91).
  • the data (rows) having the "temporal dimension, spatial dimension, eigendimension, characteristic data identifier" in the table created above are converted to the normalized temporal dimension, spatial Filter from tables of data representing dimensions, intrinsic dimensions, and properties.
  • the table of the generator is denormalized each time, and the temporal dimension, spatial dimension, It is also possible to select data (rows) that satisfy the conditions for each table of data representing unique dimensions and characteristics.
  • step S24d the selected data (rows) are deleted from the data table representing the normalized time dimension, spatial dimension, eigendimension, and characteristics of the generation destination.
  • the operation example shown in the upper part (a) of FIG. 19 describes a time dimension table, but the same applies to a space dimension, an eigendimension, and a table of data representing characteristics.
  • the operation is the same as when it is stored as non-normalized tabular data.
  • the table is created by the processing of the above substeps S24c-1 and S24c-2, in substep S24c-3, the set of "event identifiers" in the created table "identifier of the event that is the source of the deleted data” ” is replaced, and data (rows) representing the structure whose information source is the event of the identifier is selected from the generation destination table.
  • step S24d the selected data (row) is deleted from the destination table. This results in a structure similar to that of FIG.
  • FIG. 20 is a block diagram showing an example of the hardware configuration of the data analysis processing device 10 according to the embodiment.
  • the data analysis processing device 10 includes a processor 22, a memory 24, a storage 26, and an interface section . That is, the data analysis processing device 10 is composed of a computer, such as a personal computer or a server computer.
  • the processor 22 is composed of an arithmetic unit such as a Central Processing Unit (CPU) or a Micro Processing Unit (MPU).
  • CPU Central Processing Unit
  • MPU Micro Processing Unit
  • the memory 24 is a main storage device, and has, for example, RAM (Random Access Memory) and ROM (Read Only Memory).
  • the ROM stores programs and information necessary for basic processing of the processor 22 .
  • the RAM temporarily stores programs and information necessary for processing executed by the processor 22 .
  • the storage 26 is an auxiliary storage device, and is composed of non-volatile storage media such as HDD (Hard Disk Drive) and SSD (Solid State Drive).
  • Storage 26 stores multidimensional database 18 and data analysis processing program 32 .
  • the data analysis processing program 32 is a program that causes the processor 22 to execute the functions of the OLAP operation execution unit 12, the structure operation execution unit 14, and the multidimensional database management unit 16.
  • FIG. The source of the data analysis processing program 32 is, for example, an optical disk or a network.
  • the processor 22 operates as the OLAP operation execution unit 12, the structure operation execution unit 14, and the multidimensional database management unit 16 by reading the data analysis processing program 32 from the storage 26 into the memory 24 and executing it.
  • the OLAP operation execution unit 12, the structure operation execution unit 14, and the multidimensional database management unit 16 may be configured by various other elements instead of being configured by the processor 22 and memory 24.
  • the interface unit 28 is connected to the network 50.
  • the data analysis processing device 10 can receive an operation instruction from the client 40 via the network 50 and provide the client 40 with the processing result.
  • the data analysis processing device 10 associates data embodying real-world events with event identifiers for identifying the events that are information sources of the data, and creates data representing the structure of the events. In addition, it accumulates and manages in a multidimensional cube constructed for each subject. More specifically, the data analysis processing device 10 configures the multidimensional cube of the generation destination by using the data of the multidimensional cube of the generation source as the original for the multidimensional cube of the generation source and the multidimensional cube of the generation source in the OLAP operation. Based on the type of data representing the structure of the event to be generated and the value of the attribute, increase or decrease the data that constitutes the multidimensional cube of the generation destination.
  • the data analysis processing device 10 may, in addition to the type and attribute values of the data representing the structure of the events that make up the multidimensional cube of the generation destination, the time dimension and the spatial dimension that make up the multidimensional cube of the generation source. , intrinsic dimensions, and the values of data representing characteristics are also used to increase the data that make up the multidimensional cube of the destination.
  • the data analysis processing device 10 in addition to the type and attribute values of the data representing the structure of the event that constitutes the multidimensional cube of the generation destination, the time dimension, the spatial dimension, and the unique dimension that constitute the multidimensional cube of the generation destination The data that constitutes the multidimensional cube of the generation destination is reduced based on the values of the data representing the dimensions and characteristics as well.
  • the present invention is not limited to the above-described embodiments, and can be variously modified in the implementation stage without departing from the gist of the present invention. Further, each embodiment may be implemented in combination as appropriate, in which case the combined effect can be obtained. Furthermore, various inventions are included in the above embodiments, and various inventions can be extracted by combinations selected from a plurality of disclosed constituent elements. For example, even if some constituent elements are deleted from all the constituent elements shown in the embodiments, if the problem can be solved and effects can be obtained, the configuration with the constituent elements deleted can be extracted as an invention.

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WO2021044497A1 (ja) * 2019-09-02 2021-03-11 日本電信電話株式会社 データ分析処理装置、データ分析処理方法及びデータ分析処理プログラム

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